From 1373e8e059c32ebc36b98f0fcbf0e791b0c4a950 Mon Sep 17 00:00:00 2001
From: Ludovic Moncla <ludovic.moncla@insa-lyon.fr>
Date: Mon, 7 Jun 2021 14:18:20 +0200
Subject: [PATCH] update
---
evaluate_model.py | 11 -------
experimentsClassicClassifiers.py | 49 +++++++++++---------------------
2 files changed, 16 insertions(+), 44 deletions(-)
diff --git a/evaluate_model.py b/evaluate_model.py
index 8abd2e9..f258ccd 100644
--- a/evaluate_model.py
+++ b/evaluate_model.py
@@ -57,14 +57,3 @@ def evaluate_model(clf, X_test, y_test, y_pred, valid_y, classes, classesName, p
plt.savefig(pathSave)
return df, accuracy, weighted_avg
-import seaborn as sns
-import matplotlib.pyplot as plt
-from sklearn.metrics import confusion_matrix
-
-
-#y_true = [2, 0, 2, 2, 0, 1]
-#y_pred = [0, 0, 2, 2, 0, 2]
-#cf_matrix = confusion_matrix(y_true, y_pred)
-#sns.heatmap(cf_matrix, annot=True)
-#import matplotlib.pyplot as plt
-#plt.show()
diff --git a/experimentsClassicClassifiers.py b/experimentsClassicClassifiers.py
index c6a9f72..3c69915 100644
--- a/experimentsClassicClassifiers.py
+++ b/experimentsClassicClassifiers.py
@@ -96,44 +96,27 @@ for columnInput in [columnText, 'firstParagraph']:
grid_param_name, grid_param = tmp_grid_params
print(clf_name, clf, grid_param_name, grid_param)
model_file_name = columnInput + '_' +feature_technique_name + '_' + clf_name+ str(minOfInstancePerClass) + '_' + str(maxOfInstancePerClass) +".pkl"
- if clf_name == 'bayes' :
- if feature_technique_name == 'doc2vec':
- continue
- else:
- t_begin = time.time()
- # if model exist
- if os.path.isfile(os.path.join('./model', model_file_name)):
- with open(model_file_name, 'rb') as file:
- clf = pickle.load(file)
- else:
- #if model not exists we save
- with open(Pkl_Filename, 'wb') as file:
- clf.fit(train_x, train_y)
- pickle.dump(clf, file)
-
- t_end =time.time()
- training_time = t_end - t_begin
-
- y_pred = clf.predict(test_x)
-
- else :
-
+
+ if clf_name != 'bayes' :
clf = GridSearchCV(clf, grid_param, refit = True, verbose = 3)
- t_begin = time.time()
+ else if feature_technique_name == 'doc2vec':
+ continue
+
+ t_begin = time.time()
- if os.path.isfile(os.path.join('./model', model_file_name)):
- with open(model_file_name, 'rb') as file:
- clf = pickle.load(file)
- else:
- with open(Pkl_Filename, 'wb') as file:
- clf.fit(train_x, train_y)
- pickle.dump(clf, file)
+ if os.path.isfile(os.path.join('./model', model_file_name)):
+ with open(model_file_name, 'rb') as file:
+ clf = pickle.load(file)
+ else:
+ with open(Pkl_Filename, 'wb') as file:
+ clf.fit(train_x, train_y)
+ pickle.dump(clf, file)
- t_end =time.time()
+ t_end =time.time()
- training_time = t_end - t_begin
+ training_time = t_end - t_begin
- y_pred = clf.predict(test_x)
+ y_pred = clf.predict(test_x)
#evaluate model
file_name_report = columnInput + '_' +feature_technique_name + '_' + clf_name
--
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